Maternal cardiorespiratory fitness is associated with metabolic hormone concentrations in human milk

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Giskeødegård, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8639144/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The composition of human milk reflects maternal cardiometabolic health, of which cardiorespiratory fitness is a key determinant. Yet, the influence of maternal fitness on milk-borne hormones remains largely unexplored. In this cross-sectional study, we measured peak oxygen uptake during a maximal exercise test in 149 lactating participants and quantified human milk adiponectin and insulin concentrations using enzyme-linked immunosorbent assays. Higher maternal cardiorespiratory fitness was associated with lower concentrations of milk adiponectin after adjustment for body composition and weeks since delivery (β -0.02, 95% CI -0.04 to -0.001). Milk insulin concentrations were inversely associated with fitness after adjustment for body mass index (β -0.02, 95% CI -0.04 to -0.001). This work identifies maternal cardiorespiratory fitness as a previously unrecognised factor associated with human milk hormone concentrations, providing a potential pathway linking maternal physiology with early-life metabolic development. Biological sciences/Biochemistry Health sciences/Biomarkers Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Biological sciences/Physiology Figures Figure 1 Introduction Human milk is as the optimal source of nutrition for infants, providing essential nutrients and bioactive molecules that support growth, metabolic development and immune protection 1 , 2 . Among these bioactive components are hormones such as adiponectin and insulin, which are primarily derived from maternal circulation 3 , 4 , though evidence suggests that they can also be synthesized locally within mammary epithelial cells 5 . In adults, both adiponectin and insulin are key regulators of energy balance, appetite, and substrate metabolism, and accumulating evidence indicates that they may also play an important role in regulating infant metabolism and body composition 6 . Observational studies have reported inverse associations between milk adiponectin and insulin concentrations and infant growth trajectories 7 . However, findings remain inconsistent, and the determinants of these hormone concentrations in human milk are not fully understood 7 . Because the concentrations of adiponectin and insulin in human milk partly reflect maternal circulating levels, maternal cardiometabolic health may play an important role in shaping milk composition. Indeed, previous research has shown that modifiable lifestyle factors, including diet and body composition, can influence milk hormone concentrations 8 , 9 . Our group recently demonstrated that exercise can acutely alter milk hormone levels: high-intensity interval training increased human milk adiponectin 1 hour post-exercise 10 , while endurance exercise had no significant effect on milk insulin, but blunted postprandial milk insulin responses 11 . Cardiorespiratory fitness (CRF), measured as peak oxygen uptake (VO 2 peak), reflects the integrative capacity of the circulatory, respiratory, and muscular systems to deliver and utilize oxygen during sustained exercise 12 . CRF is a strong, modifiable indicator of overall health and a superior predictor of morbidity and mortality compared with physical activity levels alone 13 . Given that CRF influences metabolic processes 14 , it can modulate circulating hormone levels that reflect cardiometabolic health 15 , 16 . For instance, circulating insulin is inversely associated with CRF 17 , 18 , whereas adiponectin has shown both positive 19 , 20 and inverse 21 , 22 associations. Nevertheless, the association between CRF and human milk hormone composition has not yet been investigated. Given the central role of CRF in metabolic regulation, we aimed to determine whether maternal CRF is associated with milk hormones involved in infant metabolic programming. Specifically, we hypothesised that higher maternal CRF would be associated with higher milk adiponectin and lower milk insulin concentrations, independent of body composition. Results We included 149 participants who completed cardiorespiratory fitness testing and provided a human milk sample. Table 1 shows baseline characteristics for the pooled cohort. The majority of participants were of Norwegian ethnicity (88.7%, n = 134). Among participants enrolled in the cross-over studies, fitness testing was conducted at an average of 7.6 weeks postpartum (standard deviation (SD) 1.8), with human milk collected an average of 2.4 weeks later (SD 0.9). Table 1 Characteristics of participants. All participants ( N = 149) Age (years) 32.7 (3.9) Weeks postpartum 10.8 (1.1) Infant birth weight (grams) 3644 (424.6) Infant sex, n (female/ male) 77/74 Body composition Body mass (kg) 74.7 (12.7) Body mass index (kg/m 2 ) 26.3 (4.8) Muscle mass (kg) 28.0 (3.0) Visceral fat area (cm 2 ) 112.2 (53.9) Fat mass (kg) 24.1 (10.5) Fat percent (%) 31.2 (8.6) Cardiopulmonary exercise testing Peak oxygen uptake (mL·kg − 1 ·min − 1 ) 37.9 (7.1) Peak oxygen uptake (L·min- 1 ) 2.8 (0.4) Heart rate maximum (beats/min) 188.8 (8.1) Maximum respiratory exchange ratio* 1.2 (0.04) Rate of perceived exertion* 18.7 (2.3) Human milk hormones Adiponectin (µg/mL) 6.0 (4.3, 7.7) Insulin (µIU/L) 9.3 (6.7, 15.3) Baseline characteristics and cardiopulmonary exercise testing data are presented as means and standard deviations (SDs) or frequencies ( n ). Human milk hormone concentrations are reported as medians and 25- and 75-percentiles (quartiles). Rate of perceived exertion according to the Borg 6–20 scale 23 . *Missing data for one participant. Table 2 Regression analysis of associations between maternal peak oxygen uptake (VO 2 peak) and log-transformed human milk adiponectin and insulin concentrations. Values are beta coefficients (Beta) with 95% confidence intervals (CIs). Adiponectin Insulin Beta (95% CI) P -value Beta (95% CI) P -value Model 1 Peak oxygen uptake, mL·kg − 1 ·min − 1 -0.01 (-0.02, 0) 0.058 -0.03 (-0.05, -0.02) < .001 Model 2 Peak oxygen uptake, mL·kg − 1 ·min − 1 -0.02 (-0.03, -0.004) 0.012 -0.02 (-0.04, -0.001) 0.039 Model 3 Peak oxygen uptake, mL·kg − 1 ·min − 1 -0.02 (-0.04, -0.001) 0.038 -0.01 (-0.03, 0.01) 0.33 P -values are from univariate and multivariate linear regression analysis. Model 2 was adjusted for body mass index, and model 3 was adjusted for body mass index, fat mass, visceral fat area, and weeks since delivery. Statistically significant associations ( P < 0.05) are shown in bold. Associations between VO 2 peak and human milk adiponectin Figure 1 a shows the individual maternal peak oxygen uptake (VO 2 peak) and adiponectin concentrations for all participants. In unadjusted analysis, VO 2 peak was not statistically significantly associated with human milk adiponectin concentrations (Table 2 , model 1). In a multivariate linear regression model adjusting for maternal body mass index (BMI) (Table 2 , model 2), VO 2 peak was inversely associated with human milk adiponectin (β -0.02, 95% CI -0.03 to -0.004), corresponding to a 1.7% decrease in adiponectin per unit increase in VO 2 peak (in mL·kg − 1 ·min − 1 ). The inverse association remained significant (β -0.02, 95% CI -0.04 to -0.001) when additionally adjusted for fat mass, visceral fat area, and weeks since delivery. Associations between VO 2 peak and human milk insulin Figure 1 b illustrates individual maternal VO 2 peak and insulin concentrations for all participants. VO 2 peak was inversely associated with human milk insulin concentrations in the unadjusted model (β -0.03, 95% CI -0.05 to -0.02), corresponding to a 3.3% decrease per unit increase in VO 2 peak (Table 2 , model 1). In a multivariate linear regression model adjusting for maternal BMI (Table 2 , model 2), VO 2 peak was inversely associated with human milk insulin (β -0.02, 95% CI -0.04 to -0.001), corresponding to a 1.8% decrease in insulin per unit increase in VO 2 peak. However, the association was not statistically significant when further adjusted for fat mass, visceral fat, or weeks since delivery (Table 3 , model 3). Exploratory analysis between low vs high CRF and healthy vs high BMI As shown in Table 3 , human milk insulin concentrations were significantly higher in participants with high versus healthy BMI and in those with low versus high VO 2 peak. In contrast, milk adiponectin concentrations did not differ across BMI or VO 2 peak categories. Human milk adiponectin and insulin concentrations were not correlated ( r = 0.00, P = 0.90). Table 3 Human milk adiponectin and insulin concentrations according to maternal body mass index (healthy vs. high BMI) and peak oxygen uptake (high vs. low VO 2 peak). BMI Healthy (n = 68) High (n = 83) Effect estimate (95% CIs) P -value Adiponectin 5.9 (4.3, 7.7) 6.1 (4.4, 7.5) -0.14 (-0.46, 0.19) 0.41 Insulin 8.0 (6.1, 10.3) 12.2 (7.9, 20.3) -0.76 (-1.09, -0.42) < .001 VO 2 peak High (n = 44) Low (n = 107) Effect estimate (95% CIs) P -value Adiponectin 4.9 (3.7, 6.6) 6.3 (4.5, 7.7) 0.32 (-0.04, 0.67) 0.08 Insulin 7.7 (5.2, 9.9) 10.9 (7.3, 18.4) 0.75 (0.39, 1.11) < .001 Numbers are medians and 25- and 75-percentiles (quartiles) with effect estimates (Cohen’s d) and corresponding 95% confidence intervals (CIs) and P -values. Effect estimates, 95% CIs, and P -values and are from independent samples t- tests based on comparisons using log-transformed values. Statistically significant associations ( P < 0.05) are shown in bold. Equal variances not assumed for body mass index comparisons for log insulin. In sensitivity analyses, the interaction between VO 2 peak and study design was not significant for milk adiponectin (β -0.004, 95% CI -0.03 to 0.02) and insulin (β -0.01, 95% CI -0.04 to 0.02), indicating that the elongated period between milk sampling and the cardiorespiratory fitness test in the cross-over trials did not affect the results. Discussion This cross-sectional study examined associations between maternal CRF and human milk concentrations of adiponectin and insulin. To our knowledge, this is the first investigation to link maternal CRF with these two key metabolic hormones in human milk. Contrary to our hypothesis, we found a significant inverse association between CRF and adiponectin concentrations after adjusting for BMI, fat mass, visceral fat area, and weeks since delivery. CRF was also inversely associated with human milk insulin concentrations after adjusting for BMI alone, although this relationship was attenuated when additional covariates were included. CRF is a robust marker of cardiometabolic health, influenced by physical activity, genetics, age, body composition, sex, and smoking status 24 . Reference data from the Trøndelag Health Study (HUNT 3) indicate mean VO 2 peak values of 43.0, 40.0, and 38.4 mL·kg − 1 ·min − 1 in women aged 20–29, 30–39, 40–49, respectively 25 . In our cohort, approximately 70% of postpartum participants exhibited lower CRF than age- and region-matched counterparts, consistent with evidence of reduced fitness up to 1 year postpartum 26 , likely due to physiological and behavioural changes during and after pregnancy 27 . Given that low CRF is associated with adverse cardiometabolic profiles 28 and is a stronger predictor of morbidity and mortality than anthropometric measures alone 29 , understanding its relevance for lactation-related physiology has important clinical implications. The observed inverse relationship between CRF and human milk adiponectin suggests that maternal fitness may influence milk adiponectin through mechanisms beyond maternal adiposity, potentially reflecting systemic metabolic adaptations associated with improved fitness. Prior studies in men and adolescents have reported negative associations between circulating adiponectin and fitness 21 , 22 , consistent with our findings. Although we did not measure maternal circulating adiponectin, the alignment with systemic patterns raises the possibility that higher maternal CRF is accompanied by metabolic adaptations that extend to the mammary gland. These data highlight maternal fitness as a potential determinant of the hormonal milieu of human milk. Human milk insulin showed a similar inverse association with CRF adjusted for BMI, consistent with the established link between higher fitness, improved insulin sensitivity, and lower circulating insulin concentrations 17 , 30 . Elevated milk insulin concentrations have been reported in mothers with type 2 diabetes mellitus compared with BMI-matched mothers with gestational diabetes or normal glucose tolerance 31 , likely reflecting maternal hyperinsulinemia 32 . The attenuation of the CRF-milk insulin association after adjustment for fat mass, visceral fat area, and weeks since delivery indicates that maternal adiposity and weeks postpartum may play stronger roles in shaping milk insulin concentrations. Indeed, visceral adiposity is a stronger predictor of insulin resistance in non-diabetic people than CRF 33 , but its relevance for human milk hormone composition is yet to be elucidated. Further studies incorporating detailed body composition phenotyping, including distribution and metabolic activity of fat deposits, are needed to clarify pathways influencing lactational insulin secretion or mammary transfer. Exploratory analyses further showed higher milk insulin concentrations in participants with low vs. high CRF and in those with high vs. healthy BMI, reinforcing the notion that maternal metabolic health influences milk insulin levels. We observed no association between milk adiponectin and insulin concentrations. In circulation, adiponectin enhances insulin sensitivity, and the two hormones are typically inversely related in healthy adults 34 . The absence of this relationship in milk may reflect different concentration gradients between blood and milk, and the distinct mechanisms governing mammary transfer and secretion. Insulin concentrations in milk are generally higher than in maternal plasma, whereas adiponectin concentrations are lower 35 . These differences may arise from selective transfer mechanisms across the mammary epithelium or local synthesis and regulation within the mammary gland, suggesting that hormonal composition of human milk is not solely determined by maternal circulating levels 36 . Several limitations should be considered. We did not measure circulating maternal hormone levels, constraining our ability to directly link milk hormones to systemic physiology. Differences in milk sampling protocols across studies within the pooled dataset may also have introduced variability in hormone measurements. Furthermore, the interval between fitness testing and milk collection differed across cohorts, although sensitivity analyses and adjustment for weeks since delivery did not materially affect the primary associations and supported the validity of pooling data across studies. The predominantly Norwegian and highly educated composition of the samples, as well as the inclusion of only participants who were exclusively breastfeeding, may limit the generalisability of these findings to more diverse populations. In summary, we report that higher maternal CRF was associated with lower human milk adiponectin, independent of maternal adiposity and weeks since delivery. CRF was inversely associated with human milk insulin when adjusting for BMI alone, but this association was not independent of additional covariates of fat mass, visceral fat area, and weeks since delivery. These findings advance our understanding of how maternal cardiometabolic health shapes human milk composition and suggest that improving CRF may influence the hormonal environment experienced by the breastfeeding infant. Longitudinal and interventional studies are now needed to determine whether enhancing maternal fitness postpartum can beneficially modulate milk hormone profiles and support both maternal and infant metabolic health. Methods Study design and setting This cross-sectional study pooled data from three independent studies carried out at the Norwegian University of Science and Technology (NTNU), in Trondheim, Norway. The Regional Committee for Medical and Health Research Ethics, Central Norway (REK), approved all studies (REK-263493, 551616, 562012), and this study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to participation. We derived data from one cross-sectional study ( n = 102), and baseline data from two cross-over trials ( n = 19 and n = 28). In the cross-sectional study, milk samples and maternal health outcomes were obtained on the same day; in the cross-over trials, these assessments were conducted on separate days. To ensure comparability between studies, we analysed milk samples collected closest to 10–12 weeks postpartum across all cohorts. Participants were recruited between February 2021 and August 2025 through targeted social media advertisements and word of mouth. Eligibility screening was conducted by telephone or email. Inclusion criteria for the cross-sectional pooled data study were: (1) ability to complete an exercise test to exhaustion, (2) exclusive breastfeeding of a singleton term infant, (3) 18 years or older, and (3) 9–12 weeks postpartum. One of the cross-over trials included participants 6–12 weeks postpartum; the other included participants 5–12 weeks postpartum and required a BMI of > 25 kg/m 2 . Exclusion criteria for all three studies were known cardiovascular disease or type 1 or 2 diabetes. Reporting followed the Strengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines 37 . Human milk sampling and analysis The participants were instructed to refrain from high-intensity exercise for 48 hours prior to milk collection. On the morning of sampling, participants collected a fasted milk sample before 9:00 using an electric breast pump (Medela Symphony, Switzerland) or a personal pump following proper sterilization according to manufacturer guidelines. We instructed the participants to express milk from one breast until empty. If < 25 mL was obtained, milk could also be expressed from the opposite breast. Samples were gently mixed to minimize variability due to foremilk-hindmilk differences. A 25 mL aliquot was transferred into a sterile Falcon tube and transported on ice packs to the laboratory, where samples were immediately aliquoted and stored at -80°C until analysis. One of the cross-over studies employed a slightly modified collection protocol, as previously described 11 . In short, participants in that study were instructed to provide at least 25 mL of milk, without explicit full expression, and to store the sample in their home freezer until delivery to the laboratory. Before adiponectin quantification, we thawed the samples and centrifuged at 10,000 x g for 60 min at 4°C to remove the lipid layer. We used the skimmed milk fraction for adiponectin assays, while full milk was used for insulin measurement, based on pilot testing indicating optimal performance under these conditions. We used enzyme-link immunosorbent assay (ELISA) to determine the concentrations of adiponectin (IBL International GmBH, Germany, Catalog no: 30126762) and insulin (IBL International GmBH, Germany, Catalog no: RE53171). The intra- and inter-assay coefficient of variation were < 5% and 7.5% for adiponectin, and < 3% and 6% for insulin as detailed by the manufacturer. We utilized a Dynex DS2 automated ELISA system (Montebello Diagnostics AS, Norway), with DS-Matrix software. Based on internal optimisation, final incubation times for both assays were reduced from 15 min to 12 min. All remaining procedures followed the manufacturer instructions. Adiponectin and insulin were detected in 97% and 100% of the samples, respectively. Adiponectin values below the limit of detection (n = 4) were imputed using values derived from the ELISA curve-fit equation. Anthropometry, questionnaires, and cardiorespiratory fitness testing We measured the participants’ height using a wall-mounted stadiometer and estimated body composition using multi-frequency bioelectrical impedance analysis (InBody 770, Biospace, Seoul, South Korea). The participants completed questionnaires to report demographic and lifestyle variables, including maternal age, parity, physical activity level (average weekly hours and minutes of vigorous and moderate activity), smoking (yes/no), medication use (during and/or after pregnancy, type), and infant characteristics (birth date, birth weight, and sex). CRF was assessed with a graded treadmill test (Woodway, Germany) with continuous heart-rate monitoring (Polar, Finland). One participant completed the test on a stationary cycle ergometer (Corival, Lode, Netherlands). VO 2 peak and respiratory exchange ratio were measured via indirect calorimetry (Metalyzer II Portable CPX System, Cortex, Germany) following manufacturer-recommended calibration. After a 10-min warm-up at moderate intensity (rating of perceived exertion 11–13 on a 6–20 Borg scale 23 ), we fitted the participants with a face mask (V2 series, Hans Rudolph, USA). Individualised ramp protocols began at the final warm-up intensity and increased every 1–2 minutes by 0.5-1 km/h or 1–2% for the treadmill test, or 25 watts every 30 seconds on the cycle ergometer, until volitional exhaustion. We report VO 2 peak as some participants did not meet VO 2 max criteria 38 . VO 2 peak was calculated as the mean of the three highest consecutive VO 2 values, and maximum respiratory exchange ratio was the highest value measured from the three corresponding VO 2 measurements. Statistical methods Data are presented as means with SDs or medians and interquartile ranges. Associations between maternal VO 2 peak (mL·kg − 1 ·min − 1 ) and milk concentrations of adiponectin and insulin were examined using univariate and multivariate linear regression. We assessed the residuals and standardized residuals for normality, and log-transformed hormone concentrations to improve distributional assumptions. Initial linear regression models assessed univariate associations between VO 2 peak (independent variable) and milk hormone concentrations (dependent variable). A subsequent multivariable model was adjusted for maternal BMI, and a third model additionally adjusted for fat mass, visceral fat area, and weeks since delivery (when the milk was sampled). Regression coefficients were back-transformed (exponentiated [Y= e β1 ]) as percentage changes for interpretability. Exploratory analyses compared milk hormone concentrations between participants with a healthy (18.5–24.9 kg/m 2 ) and high (≥ 25.0 kg/m 2 ) BMI, and between participants with a low and high CRF (a low CRF was classified as a VO 2 peak lower than the average for age-specific regional reference data 25 ) using independent sample t -tests (Table 3 ). Equality of variances was met for three of the four comparisons. Correlation between milk adiponectin and insulin concentrations were evaluated using Spearman’s rho. As we measured VO 2 peak earlier postpartum in the two cross-over trials compared with the cohort from the cross-sectional study, we evaluated potential effect modification by study group. A linear regression model including VO₂peak, study group (coded as 0 = cross-sectional, 1 = cross-over), and the interaction term VO₂peak × study group was used to test interaction significance. Statistical significance was defined as P < 0.05. The statistical analyses were conducted using IBM SPSS Statistics version 30 and figures were created using Graphpad Prism 10 (Dotmatics). Declarations Competing interests The authors declare no competing interests. Author Contribution E.R.A coordinated the study, led data collection, carried out data analyses, and wrote the original draft; M.C.C.L participated in data collection, reviewed and edited the manuscript draft; G.F.G reviewed data analyses, reviewed and edited manuscript draft; T.M conceived and directed the study, acquired funding, contributed to data analyses, reviewed and edited manuscript draft. All authors approved the submitted version of the manuscript. Acknowledgement We wish to thank Guro Rosvold, Oda Fossum, Mads Holmen for data collection assistance; Øystein Røsand for assistance in ELISA analyses. Data Availability Anonymised datasets will be deposited in Zenodo data repository upon publication. References Carr, L. E. et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8639144","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":585271047,"identity":"1355bdb3-d74a-4d90-8acf-ebb6d9b37885","order_by":0,"name":"Emily Rose Ashby","email":"","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"Rose","lastName":"Ashby","suffix":""},{"id":585271050,"identity":"29bb8632-a140-4b0a-b930-f12522ecc337","order_by":1,"name":"Maëliss Cynthia Chloe Lemoine","email":"","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Maëliss","middleName":"Cynthia Chloe","lastName":"Lemoine","suffix":""},{"id":585271053,"identity":"6677e5f5-6b16-4cd3-bdfa-57efd368b8d3","order_by":2,"name":"Guro F. Giskeødegård","email":"","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guro","middleName":"F.","lastName":"Giskeødegård","suffix":""},{"id":585271060,"identity":"1f7cf8f4-14cc-40a0-acd5-0e9aaf29a743","order_by":3,"name":"Trine Moholdt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACxgYwZcDA3CDBUEGEBh6EFsYGiQNnGCSI0AIFYC0H24jQYi99uO3BB4ZtcubsjY23P867U2cukcAGFMFjC19iu+EMhtvGlj0Hmy0ObnsmYTkjgR0ogkcLD2ObNA/D7cQNNxLbJA5uOyxhcCOBTZqHkJY/cC1ziNXCANfSQIyWM4xtkj0Gt40NzgD9cubYYckNZx62SeLzC3sP+zOJHxW35QyONx+8UVFzmN/gePIxCXwhBgEGKDxo5I6CUTAKRsEoIB8AANcOTqaat7swAAAAAElFTkSuQmCC","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Trine","middleName":"","lastName":"Moholdt","suffix":""}],"badges":[],"createdAt":"2026-01-19 11:54:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8639144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8639144/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101852196,"identity":"f59910ff-97a6-4a83-bec0-f651203b1c1d","added_by":"auto","created_at":"2026-02-04 10:11:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":226407,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between peak oxygen uptake and log-transformed human milk adiponectin (a) and insulin (b).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8639144/v1/d47f9a4e958c64f8eac5673d.jpeg"},{"id":101852257,"identity":"f9c72561-ec5c-4c63-a051-c239fb6f5feb","added_by":"auto","created_at":"2026-02-04 10:11:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":753715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8639144/v1/fba532b4-4f1d-412e-ab08-95a0c3c4ac18.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Maternal cardiorespiratory fitness is associated with metabolic hormone concentrations in human milk","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman milk is as the optimal source of nutrition for infants, providing essential nutrients and bioactive molecules that support growth, metabolic development and immune protection\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Among these bioactive components are hormones such as adiponectin and insulin, which are primarily derived from maternal circulation\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, though evidence suggests that they can also be synthesized locally within mammary epithelial cells\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In adults, both adiponectin and insulin are key regulators of energy balance, appetite, and substrate metabolism, and accumulating evidence indicates that they may also play an important role in regulating infant metabolism and body composition\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Observational studies have reported inverse associations between milk adiponectin and insulin concentrations and infant growth trajectories\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, findings remain inconsistent, and the determinants of these hormone concentrations in human milk are not fully understood\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBecause the concentrations of adiponectin and insulin in human milk partly reflect maternal circulating levels, maternal cardiometabolic health may play an important role in shaping milk composition. Indeed, previous research has shown that modifiable lifestyle factors, including diet and body composition, can influence milk hormone concentrations\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Our group recently demonstrated that exercise can acutely alter milk hormone levels: high-intensity interval training increased human milk adiponectin 1 hour post-exercise\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, while endurance exercise had no significant effect on milk insulin, but blunted postprandial milk insulin responses\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCardiorespiratory fitness (CRF), measured as peak oxygen uptake (VO\u003csub\u003e2\u003c/sub\u003epeak), reflects the integrative capacity of the circulatory, respiratory, and muscular systems to deliver and utilize oxygen during sustained exercise\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. CRF is a strong, modifiable indicator of overall health and a superior predictor of morbidity and mortality compared with physical activity levels alone\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Given that CRF influences metabolic processes\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, it can modulate circulating hormone levels that reflect cardiometabolic health\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. For instance, circulating insulin is inversely associated with CRF\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, whereas adiponectin has shown both positive\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and inverse\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e associations. Nevertheless, the association between CRF and human milk hormone composition has not yet been investigated.\u003c/p\u003e \u003cp\u003eGiven the central role of CRF in metabolic regulation, we aimed to determine whether maternal CRF is associated with milk hormones involved in infant metabolic programming. Specifically, we hypothesised that higher maternal CRF would be associated with higher milk adiponectin and lower milk insulin concentrations, independent of body composition.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe included 149 participants who completed cardiorespiratory fitness testing and provided a human milk sample. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows baseline characteristics for the pooled cohort. The majority of participants were of Norwegian ethnicity (88.7%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;134). Among participants enrolled in the cross-over studies, fitness testing was conducted at an average of 7.6 weeks postpartum (standard deviation (SD) 1.8), with human milk collected an average of 2.4 weeks later (SD 0.9).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll participants\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.7 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeeks postpartum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfant birth weight (grams)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3644 (424.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfant sex, \u003cem\u003en\u003c/em\u003e (female/ male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77/74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBody composition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.7 (12.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.2 (53.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.1 (10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat percent (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.2 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCardiopulmonary exercise testing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak oxygen uptake (mL\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.9 (7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak oxygen uptake (L\u0026middot;min-\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate maximum (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188.8 (8.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum respiratory exchange ratio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate of perceived exertion*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.7 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHuman milk hormones\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin (\u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (4.3, 7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin (\u0026micro;IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3 (6.7, 15.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBaseline characteristics and cardiopulmonary exercise testing data are presented as means and standard deviations (SDs) or frequencies (\u003cem\u003en\u003c/em\u003e). Human milk hormone concentrations are reported as medians and 25- and 75-percentiles (quartiles). Rate of perceived exertion according to the Borg 6\u0026ndash;20 scale\u003csup\u003e23\u003c/sup\u003e. *Missing data for one participant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression analysis of associations between maternal peak oxygen uptake (VO\u003csub\u003e2\u003c/sub\u003epeak) and log-transformed human milk adiponectin and insulin concentrations. Values are beta coefficients (Beta) with 95% confidence intervals (CIs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAdiponectin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel 1\u003c/em\u003e\u003c/p\u003e \u003cp\u003ePeak oxygen uptake, mL\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 (-0.02, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03 (-0.05, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel 2\u003c/em\u003e\u003c/p\u003e \u003cp\u003ePeak oxygen uptake, mL\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02 (-0.03, -0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02 (-0.04, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel 3\u003c/em\u003e\u003c/p\u003e \u003cp\u003ePeak oxygen uptake, mL\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02 (-0.04, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01 (-0.03, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e-values are from univariate and multivariate linear regression analysis. Model 2 was adjusted for body mass index, and model 3 was adjusted for body mass index, fat mass, visceral fat area, and weeks since delivery. Statistically significant associations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are shown in bold.\u003c/p\u003e \u003cp\u003eAssociations between VO\u003csub\u003e2\u003c/sub\u003epeak and human milk adiponectin\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea shows the individual maternal peak oxygen uptake (VO\u003csub\u003e2\u003c/sub\u003epeak) and adiponectin concentrations for all participants. In unadjusted analysis, VO\u003csub\u003e2\u003c/sub\u003epeak was not statistically significantly associated with human milk adiponectin concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, model 1). In a multivariate linear regression model adjusting for maternal body mass index (BMI) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, model 2), VO\u003csub\u003e2\u003c/sub\u003epeak was inversely associated with human milk adiponectin (β -0.02, 95% CI -0.03 to -0.004), corresponding to a 1.7% decrease in adiponectin per unit increase in VO\u003csub\u003e2\u003c/sub\u003epeak (in mL\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The inverse association remained significant (β -0.02, 95% CI -0.04 to -0.001) when additionally adjusted for fat mass, visceral fat area, and weeks since delivery.\u003c/p\u003e \u003cp\u003eAssociations between VO\u003csub\u003e2\u003c/sub\u003epeak and human milk insulin\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb illustrates individual maternal VO\u003csub\u003e2\u003c/sub\u003epeak and insulin concentrations for all participants. VO\u003csub\u003e2\u003c/sub\u003epeak was inversely associated with human milk insulin concentrations in the unadjusted model (β -0.03, 95% CI -0.05 to -0.02), corresponding to a 3.3% decrease per unit increase in VO\u003csub\u003e2\u003c/sub\u003epeak (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, model 1). In a multivariate linear regression model adjusting for maternal BMI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, model 2), VO\u003csub\u003e2\u003c/sub\u003epeak was inversely associated with human milk insulin (β -0.02, 95% CI -0.04 to -0.001), corresponding to a 1.8% decrease in insulin per unit increase in VO\u003csub\u003e2\u003c/sub\u003epeak. However, the association was not statistically significant when further adjusted for fat mass, visceral fat, or weeks since delivery (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, model 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExploratory analysis between low vs high CRF and healthy vs high BMI\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, human milk insulin concentrations were significantly higher in participants with high versus healthy BMI and in those with low versus high VO\u003csub\u003e2\u003c/sub\u003epeak. In contrast, milk adiponectin concentrations did not differ across BMI or VO\u003csub\u003e2\u003c/sub\u003epeak categories. Human milk adiponectin and insulin concentrations were not correlated (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHuman milk adiponectin and insulin concentrations according to maternal body mass index (healthy vs. high BMI) and peak oxygen uptake (high vs. low VO\u003csub\u003e2\u003c/sub\u003epeak).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy (n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect estimate (95% CIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9 (4.3, 7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1 (4.4, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14 (-0.46, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (6.1, 10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2 (7.9, 20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.76 (-1.09, -0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003epeak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect estimate (95% CIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9 (3.7, 6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3 (4.5, 7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (-0.04, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7 (5.2, 9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9 (7.3, 18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.39, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNumbers are medians and 25- and 75-percentiles (quartiles) with effect estimates (Cohen\u0026rsquo;s d) and corresponding 95% confidence intervals (CIs) and \u003cem\u003eP\u003c/em\u003e-values. Effect estimates, 95% CIs, and \u003cem\u003eP\u003c/em\u003e-values and are from independent samples \u003cem\u003et-\u003c/em\u003etests based on comparisons using log-transformed values. Statistically significant associations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are shown in bold. Equal variances not assumed for body mass index comparisons for log insulin.\u003c/p\u003e \u003cp\u003eIn sensitivity analyses, the interaction between VO\u003csub\u003e2\u003c/sub\u003epeak and study design was not significant for milk adiponectin (β -0.004, 95% CI -0.03 to 0.02) and insulin (β -0.01, 95% CI -0.04 to 0.02), indicating that the elongated period between milk sampling and the cardiorespiratory fitness test in the cross-over trials did not affect the results.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study examined associations between maternal CRF and human milk concentrations of adiponectin and insulin. To our knowledge, this is the first investigation to link maternal CRF with these two key metabolic hormones in human milk. Contrary to our hypothesis, we found a significant inverse association between CRF and adiponectin concentrations after adjusting for BMI, fat mass, visceral fat area, and weeks since delivery. CRF was also inversely associated with human milk insulin concentrations after adjusting for BMI alone, although this relationship was attenuated when additional covariates were included.\u003c/p\u003e \u003cp\u003eCRF is a robust marker of cardiometabolic health, influenced by physical activity, genetics, age, body composition, sex, and smoking status\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Reference data from the Tr\u0026oslash;ndelag Health Study (HUNT 3) indicate mean VO\u003csub\u003e2\u003c/sub\u003epeak values of 43.0, 40.0, and 38.4 mL\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in women aged 20\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, respectively\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In our cohort, approximately 70% of postpartum participants exhibited lower CRF than age- and region-matched counterparts, consistent with evidence of reduced fitness up to 1 year postpartum\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, likely due to physiological and behavioural changes during and after pregnancy\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Given that low CRF is associated with adverse cardiometabolic profiles\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and is a stronger predictor of morbidity and mortality than anthropometric measures alone\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, understanding its relevance for lactation-related physiology has important clinical implications.\u003c/p\u003e \u003cp\u003eThe observed inverse relationship between CRF and human milk adiponectin suggests that maternal fitness may influence milk adiponectin through mechanisms beyond maternal adiposity, potentially reflecting systemic metabolic adaptations associated with improved fitness. Prior studies in men and adolescents have reported negative associations between circulating adiponectin and fitness\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, consistent with our findings. Although we did not measure maternal circulating adiponectin, the alignment with systemic patterns raises the possibility that higher maternal CRF is accompanied by metabolic adaptations that extend to the mammary gland. These data highlight maternal fitness as a potential determinant of the hormonal milieu of human milk.\u003c/p\u003e \u003cp\u003eHuman milk insulin showed a similar inverse association with CRF adjusted for BMI, consistent with the established link between higher fitness, improved insulin sensitivity, and lower circulating insulin concentrations\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Elevated milk insulin concentrations have been reported in mothers with type 2 diabetes mellitus compared with BMI-matched mothers with gestational diabetes or normal glucose tolerance\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, likely reflecting maternal hyperinsulinemia\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The attenuation of the CRF-milk insulin association after adjustment for fat mass, visceral fat area, and weeks since delivery indicates that maternal adiposity and weeks postpartum may play stronger roles in shaping milk insulin concentrations. Indeed, visceral adiposity is a stronger predictor of insulin resistance in non-diabetic people than CRF\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, but its relevance for human milk hormone composition is yet to be elucidated. Further studies incorporating detailed body composition phenotyping, including distribution and metabolic activity of fat deposits, are needed to clarify pathways influencing lactational insulin secretion or mammary transfer.\u003c/p\u003e \u003cp\u003eExploratory analyses further showed higher milk insulin concentrations in participants with low vs. high CRF and in those with high vs. healthy BMI, reinforcing the notion that maternal metabolic health influences milk insulin levels. We observed no association between milk adiponectin and insulin concentrations. In circulation, adiponectin enhances insulin sensitivity, and the two hormones are typically inversely related in healthy adults\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The absence of this relationship in milk may reflect different concentration gradients between blood and milk, and the distinct mechanisms governing mammary transfer and secretion. Insulin concentrations in milk are generally higher than in maternal plasma, whereas adiponectin concentrations are lower \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. These differences may arise from selective transfer mechanisms across the mammary epithelium or local synthesis and regulation within the mammary gland, suggesting that hormonal composition of human milk is not solely determined by maternal circulating levels\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. We did not measure circulating maternal hormone levels, constraining our ability to directly link milk hormones to systemic physiology. Differences in milk sampling protocols across studies within the pooled dataset may also have introduced variability in hormone measurements. Furthermore, the interval between fitness testing and milk collection differed across cohorts, although sensitivity analyses and adjustment for weeks since delivery did not materially affect the primary associations and supported the validity of pooling data across studies. The predominantly Norwegian and highly educated composition of the samples, as well as the inclusion of only participants who were exclusively breastfeeding, may limit the generalisability of these findings to more diverse populations.\u003c/p\u003e \u003cp\u003eIn summary, we report that higher maternal CRF was associated with lower human milk adiponectin, independent of maternal adiposity and weeks since delivery. CRF was inversely associated with human milk insulin when adjusting for BMI alone, but this association was not independent of additional covariates of fat mass, visceral fat area, and weeks since delivery. These findings advance our understanding of how maternal cardiometabolic health shapes human milk composition and suggest that improving CRF may influence the hormonal environment experienced by the breastfeeding infant. Longitudinal and interventional studies are now needed to determine whether enhancing maternal fitness postpartum can beneficially modulate milk hormone profiles and support both maternal and infant metabolic health.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eStudy design and setting\u003c/p\u003e \u003cp\u003eThis cross-sectional study pooled data from three independent studies carried out at the Norwegian University of Science and Technology (NTNU), in Trondheim, Norway. The Regional Committee for Medical and Health Research Ethics, Central Norway (REK), approved all studies (REK-263493, 551616, 562012), and this study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to participation. We derived data from one cross-sectional study (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;102), and baseline data from two cross-over trials (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19 and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28). In the cross-sectional study, milk samples and maternal health outcomes were obtained on the same day; in the cross-over trials, these assessments were conducted on separate days. To ensure comparability between studies, we analysed milk samples collected closest to 10\u0026ndash;12 weeks postpartum across all cohorts.\u003c/p\u003e \u003cp\u003eParticipants were recruited between February 2021 and August 2025 through targeted social media advertisements and word of mouth. Eligibility screening was conducted by telephone or email. Inclusion criteria for the cross-sectional pooled data study were: (1) ability to complete an exercise test to exhaustion, (2) exclusive breastfeeding of a singleton term infant, (3) 18 years or older, and (3) 9\u0026ndash;12 weeks postpartum. One of the cross-over trials included participants 6\u0026ndash;12 weeks postpartum; the other included participants 5\u0026ndash;12 weeks postpartum and required a BMI of \u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e. Exclusion criteria for all three studies were known cardiovascular disease or type 1 or 2 diabetes. Reporting followed the Strengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHuman milk sampling and analysis\u003c/p\u003e \u003cp\u003eThe participants were instructed to refrain from high-intensity exercise for 48 hours prior to milk collection. On the morning of sampling, participants collected a fasted milk sample before 9:00 using an electric breast pump (Medela Symphony, Switzerland) or a personal pump following proper sterilization according to manufacturer guidelines. We instructed the participants to express milk from one breast until empty. If\u0026thinsp;\u0026lt;\u0026thinsp;25 mL was obtained, milk could also be expressed from the opposite breast. Samples were gently mixed to minimize variability due to foremilk-hindmilk differences. A 25 mL aliquot was transferred into a sterile Falcon tube and transported on ice packs to the laboratory, where samples were immediately aliquoted and stored at -80\u0026deg;C until analysis. One of the cross-over studies employed a slightly modified collection protocol, as previously described\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In short, participants in that study were instructed to provide at least 25 mL of milk, without explicit full expression, and to store the sample in their home freezer until delivery to the laboratory.\u003c/p\u003e \u003cp\u003eBefore adiponectin quantification, we thawed the samples and centrifuged at 10,000 x g for 60 min at 4\u0026deg;C to remove the lipid layer. We used the skimmed milk fraction for adiponectin assays, while full milk was used for insulin measurement, based on pilot testing indicating optimal performance under these conditions. We used enzyme-link immunosorbent assay (ELISA) to determine the concentrations of adiponectin (IBL International GmBH, Germany, Catalog no: 30126762) and insulin (IBL International GmBH, Germany, Catalog no: RE53171). The intra- and inter-assay coefficient of variation were \u0026lt;\u0026thinsp;5% and 7.5% for adiponectin, and \u0026lt;\u0026thinsp;3% and 6% for insulin as detailed by the manufacturer. We utilized a Dynex DS2 automated ELISA system (Montebello Diagnostics AS, Norway), with DS-Matrix software. Based on internal optimisation, final incubation times for both assays were reduced from 15 min to 12 min. All remaining procedures followed the manufacturer instructions. Adiponectin and insulin were detected in 97% and 100% of the samples, respectively. Adiponectin values below the limit of detection (n\u0026thinsp;=\u0026thinsp;4) were imputed using values derived from the ELISA curve-fit equation.\u003c/p\u003e \u003cp\u003eAnthropometry, questionnaires, and cardiorespiratory fitness testing\u003c/p\u003e \u003cp\u003eWe measured the participants\u0026rsquo; height using a wall-mounted stadiometer and estimated body composition using multi-frequency bioelectrical impedance analysis (InBody 770, Biospace, Seoul, South Korea). The participants completed questionnaires to report demographic and lifestyle variables, including maternal age, parity, physical activity level (average weekly hours and minutes of vigorous and moderate activity), smoking (yes/no), medication use (during and/or after pregnancy, type), and infant characteristics (birth date, birth weight, and sex).\u003c/p\u003e \u003cp\u003eCRF was assessed with a graded treadmill test (Woodway, Germany) with continuous heart-rate monitoring (Polar, Finland). One participant completed the test on a stationary cycle ergometer (Corival, Lode, Netherlands). VO\u003csub\u003e2\u003c/sub\u003epeak and respiratory exchange ratio were measured via indirect calorimetry (Metalyzer II Portable CPX System, Cortex, Germany) following manufacturer-recommended calibration. After a 10-min warm-up at moderate intensity (rating of perceived exertion 11\u0026ndash;13 on a 6\u0026ndash;20 Borg scale\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e), we fitted the participants with a face mask (V2 series, Hans Rudolph, USA). Individualised ramp protocols began at the final warm-up intensity and increased every 1\u0026ndash;2 minutes by 0.5-1 km/h or 1\u0026ndash;2% for the treadmill test, or 25 watts every 30 seconds on the cycle ergometer, until volitional exhaustion. We report VO\u003csub\u003e2\u003c/sub\u003epeak as some participants did not meet VO\u003csub\u003e2\u003c/sub\u003emax criteria\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. VO\u003csub\u003e2\u003c/sub\u003epeak was calculated as the mean of the three highest consecutive VO\u003csub\u003e2\u003c/sub\u003e values, and maximum respiratory exchange ratio was the highest value measured from the three corresponding VO\u003csub\u003e2\u003c/sub\u003e measurements.\u003c/p\u003e \u003cp\u003eStatistical methods\u003c/p\u003e \u003cp\u003eData are presented as means with SDs or medians and interquartile ranges. Associations between maternal VO\u003csub\u003e2\u003c/sub\u003epeak (mL\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and milk concentrations of adiponectin and insulin were examined using univariate and multivariate linear regression. We assessed the residuals and standardized residuals for normality, and log-transformed hormone concentrations to improve distributional assumptions.\u003c/p\u003e \u003cp\u003eInitial linear regression models assessed univariate associations between VO\u003csub\u003e2\u003c/sub\u003epeak (independent variable) and milk hormone concentrations (dependent variable). A subsequent multivariable model was adjusted for maternal BMI, and a third model additionally adjusted for fat mass, visceral fat area, and weeks since delivery (when the milk was sampled). Regression coefficients were back-transformed (exponentiated [Y= \u003cem\u003ee\u003c/em\u003e\u003csup\u003eβ1\u003c/sup\u003e]) as percentage changes for interpretability.\u003c/p\u003e \u003cp\u003eExploratory analyses compared milk hormone concentrations between participants with a healthy (18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e) and high (\u0026ge;\u0026thinsp;25.0 kg/m\u003csup\u003e2\u003c/sup\u003e) BMI, and between participants with a low and high CRF (a low CRF was classified as a VO\u003csub\u003e2\u003c/sub\u003epeak lower than the average for age-specific regional reference data\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e) using independent sample \u003cem\u003et\u003c/em\u003e-tests (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Equality of variances was met for three of the four comparisons. Correlation between milk adiponectin and insulin concentrations were evaluated using Spearman\u0026rsquo;s rho.\u003c/p\u003e \u003cp\u003eAs we measured VO\u003csub\u003e2\u003c/sub\u003epeak earlier postpartum in the two cross-over trials compared with the cohort from the cross-sectional study, we evaluated potential effect modification by study group. A linear regression model including VO₂peak, study group (coded as 0\u0026thinsp;=\u0026thinsp;cross-sectional, 1\u0026thinsp;=\u0026thinsp;cross-over), and the interaction term VO₂peak \u0026times; study group was used to test interaction significance. Statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The statistical analyses were conducted using IBM SPSS Statistics version 30 and figures were created using Graphpad Prism 10 (Dotmatics).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eE.R.A coordinated the study, led data collection, carried out data analyses, and wrote the original draft; M.C.C.L participated in data collection, reviewed and edited the manuscript draft; G.F.G reviewed data analyses, reviewed and edited manuscript draft; T.M conceived and directed the study, acquired funding, contributed to data analyses, reviewed and edited manuscript draft. All authors approved the submitted version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe wish to thank Guro Rosvold, Oda Fossum, Mads Holmen for data collection assistance; \u0026Oslash;ystein R\u0026oslash;sand for assistance in ELISA analyses.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAnonymised datasets will be deposited in Zenodo data repository upon publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCarr, L. E. \u003cem\u003eet al.\u003c/em\u003e Role of Human Milk Bioactives on Infants\u0026rsquo; Gut and Immune Health. \u003cem\u003eFront. Immunol.\u003c/em\u003e 12, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi, D. Y. \u0026amp; Kim, S. Y. 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End Criteria for Reaching Maximal Oxygen Uptake Must Be Strict and Adjusted to Sex and Age: A Cross-Sectional Study. \u003cem\u003ePLOS ONE\u003c/em\u003e 9, e85276 (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-exercise-medicine-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Exercise Medicine and Health](https://www.nature.com/npjexercisemed/)","snPcode":"44437","submissionUrl":"https://submission.nature.com/new-submission/44437/3","title":"npj Exercise Medicine and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8639144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8639144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe composition of human milk reflects maternal cardiometabolic health, of which cardiorespiratory fitness is a key determinant. Yet, the influence of maternal fitness on milk-borne hormones remains largely unexplored. In this cross-sectional study, we measured peak oxygen uptake during a maximal exercise test in 149 lactating participants and quantified human milk adiponectin and insulin concentrations using enzyme-linked immunosorbent assays. Higher maternal cardiorespiratory fitness was associated with lower concentrations of milk adiponectin after adjustment for body composition and weeks since delivery (β -0.02, 95% CI -0.04 to -0.001). Milk insulin concentrations were inversely associated with fitness after adjustment for body mass index (β -0.02, 95% CI -0.04 to -0.001). This work identifies maternal cardiorespiratory fitness as a previously unrecognised factor associated with human milk hormone concentrations, providing a potential pathway linking maternal physiology with early-life metabolic development.\u003c/p\u003e","manuscriptTitle":"Maternal cardiorespiratory fitness is associated with metabolic hormone concentrations in human milk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 10:08:50","doi":"10.21203/rs.3.rs-8639144/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T11:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106773760657654226036402689662867231568","date":"2026-05-03T01:02:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"807833548534620677723408089131835216","date":"2026-04-30T14:06:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69370086389641967641808858018372099239","date":"2026-03-03T11:09:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T12:56:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124356928019785462604888306984011588378","date":"2026-02-26T13:24:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25866913479627329037254186807814998648","date":"2026-02-12T13:21:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T09:48:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T07:43:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-23T15:33:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Exercise Medicine and Health","date":"2026-01-19T11:44:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-exercise-medicine-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Exercise Medicine and Health](https://www.nature.com/npjexercisemed/)","snPcode":"44437","submissionUrl":"https://submission.nature.com/new-submission/44437/3","title":"npj Exercise Medicine and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37c70827-9373-4e7b-890e-8cd5fadf599d","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T11:47:15+00:00","index":31,"fulltext":""},{"type":"reviewerAgreed","content":"106773760657654226036402689662867231568","date":"2026-05-03T01:02:56+00:00","index":30,"fulltext":""},{"type":"reviewerAgreed","content":"807833548534620677723408089131835216","date":"2026-04-30T14:06:13+00:00","index":29,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62265638,"name":"Biological sciences/Biochemistry"},{"id":62265639,"name":"Health sciences/Biomarkers"},{"id":62265640,"name":"Health sciences/Endocrinology"},{"id":62265641,"name":"Health sciences/Health care"},{"id":62265642,"name":"Health sciences/Medical research"},{"id":62265643,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-02-04T10:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 10:08:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8639144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8639144","identity":"rs-8639144","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}